2,405 research outputs found
Semantic-based policy engineering for autonomic systems
This paper presents some important directions in the use of ontology-based semantics in achieving the vision of Autonomic Communications. We examine the requirements of Autonomic Communication with a focus on the demanding needs of ubiquitous computing environments, with an emphasis on the requirements shared with Autonomic Computing. We observe that ontologies provide a strong mechanism for addressing the heterogeneity in user task requirements, managed resources, services and context. We then present two complimentary approaches that exploit ontology-based knowledge in support of autonomic communications: service-oriented models for policy engineering and dynamic semantic queries using content-based networks. The paper concludes with a discussion of the major research challenges such approaches raise
Machine-assisted Cyber Threat Analysis using Conceptual Knowledge Discovery
Over the last years, computer networks have evolved into highly dynamic and interconnected environments, involving multiple heterogeneous devices and providing a myriad of services on top of them. This complex landscape has made it extremely difficult for security administrators to keep accurate and be effective in protecting their systems against cyber threats. In this paper, we describe our vision and scientific posture on how artificial intelligence techniques and a smart use of security knowledge may assist system administrators in better defending their networks. To that end, we put forward a research roadmap involving three complimentary axes, namely, (I) the use of FCA-based mechanisms for managing configuration vulnerabilities, (II) the exploitation of knowledge representation techniques for automated security reasoning, and (III) the design of a cyber threat intelligence mechanism as a CKDD process. Then, we describe a machine-assisted process for cyber threat analysis which provides a holistic perspective of how these three research axes are integrated together
Towards a Framework for Managing Inconsistencies in Systems of Systems
The growth in the complexity of software systems has led to a proliferation of systems that have been created independently to provide specific functions, such as activity tracking, household energy management or personal nutrition assistance. The runtime composition of these individual systems into Systems of Systems (SoSs) enables support for more sophisticated functionality that cannot be provided by individual constituent systems on their own. However, in order to realize the benefits of these functionalities it is necessary to address a number of challenges associated with SoSs, including, but not limited to, operational and managerial independence, geographic distribution of participating systems, evolutionary development, and emergent conflicting behavior that can occur due interactions between the requirements of the participating systems. In this paper, we present a framework for conflict management in SoSs. The management of conflicting requirements involves four steps, namely (a) overlap detection, (b) conflict identification, (c) conflict diagnosis, and (d) conflict resolution based on the use of a utility function. The framework uses a Monitor-Analyze-Plan- Execute- Knowledge (MAPE-K) architectural pattern. In order to illustrate the work, we use an example SoS ecosystem designed to support food security at different levels of granularity
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
Ontology-based collaborative framework for disaster recovery scenarios
This paper aims at designing of adaptive framework for supporting
collaborative work of different actors in public safety and disaster recovery
missions. In such scenarios, firemen and robots interact to each other to reach
a common goal; firemen team is equipped with smart devices and robots team is
supplied with communication technologies, and should carry on specific tasks.
Here, reliable connection is mandatory to ensure the interaction between
actors. But wireless access network and communication resources are vulnerable
in the event of a sudden unexpected change in the environment. Also, the
continuous change in the mission requirements such as inclusion/exclusion of
new actor, changing the actor's priority and the limitations of smart devices
need to be monitored. To perform dynamically in such case, the presented
framework is based on a generic multi-level modeling approach that ensures
adaptation handled by semantic modeling. Automated self-configuration is driven
by rule-based reconfiguration policies through ontology
Interdisciplinary perspectives on the development, integration and application of cognitive ontologies
We discuss recent progress in the development of cognitive ontologies and summarize three challenges in the coordinated development and application of these resources. Challenge 1 is to adopt a standardized definition for cognitive processes. We describe three possibilities and recommend one that is consistent with the standard view in cognitive and biomedical sciences. Challenge 2 is harmonization. Gaps and conflicts in representation must be resolved so that these resources can be combined for mark-up and interpretation of multi-modal data. Finally, Challenge 3 is to test the utility of these resources for large-scale annotation of data, search and query, and knowledge discovery and integration. As term definitions are tested and revised, harmonization should enable coordinated updates across ontologies. However, the true test of these definitions will be in their community-wide adoption which will test whether they support valid inferences about psychological and neuroscientific data
An Ontology-Based Autonomic System for Improving Data Warehouse Performances
International audienceWith the increase in the amount and complexity of information, data warehouse performance has become a constant issue, especially for decision support systems. As decisional experts are faced with the management of more complex data warehouses, a need for autonomic management capabilities is shown to help them in their work. Implementing autonomic managers over knowledge bases to manage them is a solution that we find more and more used in business intelligence environments. What we propose, as decisional system experts, is an autonomic system for analyzing and improving data warehouse cache memory allocations in a client environment. The system formalizes aspects of the knowledge involved in the process of decision making (from system hardware specifications to practices describing cache allocation) into the same knowledge base in the form of ontologies, analyzes the current performance level (such as query average response time values) and proposes new cache allocation values so that better performance is obtained
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